Hello! In this course, we will discuss the Bellman-Ford algorithm using Swift. The Bellman-Ford algorithm is designed to find the shortest path from a single source to all vertices in a graph, and it has the advantage of being usable in graphs with negative edge weights. In this lecture, we will explain the concept of the Bellman-Ford algorithm and examine the specific implementation process of the algorithm.
1. Overview of the Bellman-Ford Algorithm
The Bellman-Ford algorithm was developed by Richard Bellman and Lester Ford in 1958. The algorithm operates in the following manner:
- Updates the initialized distance array for all edges in the graph.
- Performs relaxation of edges up to the maximum number of vertices – 1 to optimize the distance information.
- Adds a negative cycle check to verify if a negative cycle exists.
1.1. Algorithm Procedure
- Set the initial distance to infinity and initialize the starting vertex’s distance to 0.
- Perform relaxation for all edges up to a maximum of V-1 times.
- Finally, check for negative cycles on all edges.
2. Defining the Problem
Now let’s define a problem where the Bellman-Ford algorithm can be used.
Problem: Finding the Shortest Path
Given the number of vertices in the graph
V
, the number of edgesE
, and the information of the edges, find the shortest path from the starting vertex to all other vertices. If the graph contains a negative cycle, output “Negative Cycle”.
Input Format
V (number of vertices) E (number of edges) Edge information (starting vertex, ending vertex, weight)
Output Format
Shortest distance to each vertex or "Negative Cycle"
3. Implementation of the Bellman-Ford Algorithm
Now that we have defined the problem, let’s implement the algorithm using Swift.
import Foundation
struct Edge {
let from: Int
let to: Int
let weight: Int
}
func bellmanFord(vertices: Int, edges: [Edge], start: Int) -> [Int] {
var distances = Array(repeating: Int.max, count: vertices)
distances[start] = 0
// Relaxation phase
for _ in 0..
3.1. Example Usage
Now let's test the above algorithm using a graph with multiple edges and vertices.
let edges = [
Edge(from: 0, to: 1, weight: 4),
Edge(from: 0, to: 2, weight: 1),
Edge(from: 2, to: 1, weight: 2),
Edge(from: 1, to: 3, weight: 1),
Edge(from: 2, to: 3, weight: 5)
]
let result = bellmanFord(vertices: 4, edges: edges, start: 0)
if !result.isEmpty {
for (index, distance) in result.enumerated() {
print("Vertex \(index): \(distance)")
}
}
4. Time Complexity of the Algorithm
The Bellman-Ford algorithm has a time complexity of O(VE) in the worst case, where there are V vertices and E edges. As a result, it can be efficiently used on relatively small graphs, but it may take a long time when the number of edges increases.
5. Conclusion
In this lecture, we covered the basic concept of the Bellman-Ford algorithm, defined the problem using it, implemented the algorithm, and explored an example. We learned that the Bellman-Ford algorithm is a valuable algorithm that can find the shortest path even in graphs with negative weight. I encourage you to use this algorithm to solve various problems.
In the next session, we will cover Dijkstra's algorithm. Thank you!